An extension of iStar for Machine Learning requirements by following the PRISE methodology
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Título: | An extension of iStar for Machine Learning requirements by following the PRISE methodology |
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Autor/es: | Barrera, Jose Manuel | Reina Reina, Alejandro | Lavalle, Ana | Maté, Alejandro | Trujillo, Juan |
Grupo/s de investigación o GITE: | Lucentia |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos |
Palabras clave: | Requirements engineering | Machine learning | iStar | Conceptual modeling |
Fecha de publicación: | 31-oct-2023 |
Editor: | Elsevier |
Cita bibliográfica: | Computer Standards & Interfaces. 2024, 88: 103806. https://doi.org/10.1016/j.csi.2023.103806 |
Resumen: | The rise of Artificial Intelligence (AI) and Deep Learning has led to Machine Learning (ML) becoming a common practice in academia and enterprise. However, a successful ML project requires deep domain knowledge as well as expertise in a plethora of algorithms and data processing techniques. This leads to a stronger dependency and need for communication between developers and stakeholders where numerous requirements come into play. More specifically, in addition to functional requirements such as the output of the model (e.g. classification, clustering or regression), ML projects need to pay special attention to a number of non-functional and quality aspects particular to ML. These include explainability, noise robustness or equity among others. Failure to identify and consider these aspects will lead to inadequate algorithm selection and the failure of the project. In this sense, capturing ML requirements becomes critical. Unfortunately, there is currently an absence of ML requirements modeling approaches. Therefore, in this paper we present the first i* extension for capturing ML requirements and apply it to two real-world projects. Our study covers two main objectives for ML requirements: (i) allows domain experts to specify objectives and quality aspects to be met by the ML solution, and (ii) facilitates the selection and justification of the most adequate ML approaches. Our case studies show that our work enables better ML algorithm selection, preprocessing implementation tailored to each algorithm, and aids in identifying missing data. In addition, they also demonstrate the flexibility of our study to adapt to different domains. |
Patrocinador/es: | This work has been co-funded by the AETHER-UA project (PID2020-112540RB-C43), a smart data holistic approach for context-aware data analytics: smarter machine learning for business modeling and analytics, funded by the Spanish Ministry of Science and Innovation. And the BALLADEER (PROMETEO/2021/088) project, a Big Data analytical platform for the diagnosis and treatment of Attention Deficit Hyperactivity Disorder (ADHD) featuring extended reality, funded by the Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital (Generalitat Valenciana). A. Reina-Reina (I-PI 13/20) hold Industrial PhD Grants co-funded by the University of Alicante and the Lucentia Lab Spin-off Company. |
URI: | http://hdl.handle.net/10045/138184 |
ISSN: | 0920-5489 (Print) | 1872-7018 (Online) |
DOI: | 10.1016/j.csi.2023.103806 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1016/j.csi.2023.103806 |
Aparece en las colecciones: | INV - LUCENTIA - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
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Barrera_etal_2024_ComputStandInterf.pdf | 1,77 MB | Adobe PDF | Abrir Vista previa | |
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